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  <h1>Source code for nlp_architect.utils.metrics</h1><div class="highlight"><pre>
<span></span><span class="c1"># ******************************************************************************</span>
<span class="c1"># Copyright 2017-2018 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1"># ******************************************************************************</span>

<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">unicode_literals</span>

<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">pearsonr</span><span class="p">,</span> <span class="n">spearmanr</span>
<span class="kn">from</span> <span class="nn">seqeval.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span> <span class="n">f1_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span> <span class="k">as</span> <span class="n">classification_f1_score</span>


<div class="viewcode-block" id="get_conll_scores"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.metrics.get_conll_scores">[docs]</a><span class="k">def</span> <span class="nf">get_conll_scores</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_lex</span><span class="p">,</span> <span class="n">unk</span><span class="o">=</span><span class="s2">&quot;O&quot;</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Get Conll style scores (precision, recall, f1)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
        <span class="n">predictions</span> <span class="o">=</span> <span class="n">predictions</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    <span class="n">test_p</span> <span class="o">=</span> <span class="n">predictions</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_p</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">test_p</span> <span class="o">=</span> <span class="n">test_p</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">test_y</span> <span class="o">=</span> <span class="n">y</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">test_y</span> <span class="o">=</span> <span class="n">test_y</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>

    <span class="n">prediction_data</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">test_y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
        <span class="n">test_yval</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">test_y</span><span class="p">[</span><span class="n">n</span><span class="p">]):</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="n">test_yval</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_lex</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
                <span class="k">pass</span>
        <span class="n">test_pval</span> <span class="o">=</span> <span class="p">[</span><span class="n">unk</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_yval</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">e</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">test_p</span><span class="p">[</span><span class="n">n</span><span class="p">])[:</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_pval</span><span class="p">)]):</span>
            <span class="k">try</span><span class="p">:</span>
                <span class="n">test_pval</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_lex</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
                <span class="k">pass</span>
        <span class="n">prediction_data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">test_yval</span><span class="p">,</span> <span class="n">test_pval</span><span class="p">))</span>
    <span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">prediction_data</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">classification_report</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">digits</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span></div>


<div class="viewcode-block" id="simple_accuracy"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.metrics.simple_accuracy">[docs]</a><span class="k">def</span> <span class="nf">simple_accuracy</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;return simple accuracy</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">preds</span> <span class="o">==</span> <span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>


<div class="viewcode-block" id="accuracy"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.metrics.accuracy">[docs]</a><span class="k">def</span> <span class="nf">accuracy</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;return simple accuracy in expected dict format</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">acc</span> <span class="o">=</span> <span class="n">simple_accuracy</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
    <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">acc</span><span class="p">}</span></div>


<div class="viewcode-block" id="acc_and_f1"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.metrics.acc_and_f1">[docs]</a><span class="k">def</span> <span class="nf">acc_and_f1</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;return accuracy and f1 score</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">acc</span> <span class="o">=</span> <span class="n">simple_accuracy</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
    <span class="n">f1</span> <span class="o">=</span> <span class="n">classification_f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">y_pred</span><span class="o">=</span><span class="n">preds</span><span class="p">)</span>
    <span class="k">return</span> <span class="p">{</span>
        <span class="s2">&quot;acc&quot;</span><span class="p">:</span> <span class="n">acc</span><span class="p">,</span>
        <span class="s2">&quot;f1&quot;</span><span class="p">:</span> <span class="n">f1</span><span class="p">,</span>
        <span class="s2">&quot;acc_and_f1&quot;</span><span class="p">:</span> <span class="p">(</span><span class="n">acc</span> <span class="o">+</span> <span class="n">f1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
    <span class="p">}</span></div>


<div class="viewcode-block" id="pearson_and_spearman"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.metrics.pearson_and_spearman">[docs]</a><span class="k">def</span> <span class="nf">pearson_and_spearman</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;get pearson and spearman correlation</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">pearson_corr</span> <span class="o">=</span> <span class="n">pearsonr</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">spearman_corr</span> <span class="o">=</span> <span class="n">spearmanr</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
    <span class="k">return</span> <span class="p">{</span>
        <span class="s2">&quot;pearson&quot;</span><span class="p">:</span> <span class="n">pearson_corr</span><span class="p">,</span>
        <span class="s2">&quot;spearmanr&quot;</span><span class="p">:</span> <span class="n">spearman_corr</span><span class="p">,</span>
        <span class="s2">&quot;corr&quot;</span><span class="p">:</span> <span class="p">(</span><span class="n">pearson_corr</span> <span class="o">+</span> <span class="n">spearman_corr</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
    <span class="p">}</span></div>


<div class="viewcode-block" id="tagging"><a class="viewcode-back" href="../../../generated_api/nlp_architect.utils.html#nlp_architect.utils.metrics.tagging">[docs]</a><span class="k">def</span> <span class="nf">tagging</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="n">p</span> <span class="o">=</span> <span class="n">precision_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span>
    <span class="n">r</span> <span class="o">=</span> <span class="n">recall_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span>
    <span class="n">f1</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">p</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">f1</span></div>
</pre></div>

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